from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowerCamelCase__  =					logging.get_logger(__name__)




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''input_features''', '''attention_mask''']







 def __init__(		self				:   Optional[int]							, lowercase_				:   Optional[Any]=80							, lowercase_				:   List[Any]=16000							, lowercase_				:   Tuple=0.0							, lowercase_				:   Any=10							, lowercase_				:   Optional[Any]=25							, lowercase_				:   str="hamming_window"							, lowercase_				:   int=3_27_68.0							, lowercase_				:   Any=0.97							, lowercase_				:   Optional[Any]=1.0							, lowercase_				:   Optional[int]=True							, lowercase_				:   List[Any]=True							, lowercase_				:   Optional[Any]=False							, **lowercase_				:   str							, )						->	str:


   """simple docstring"""







   super().__init__(feature_size=lowercase_							, sampling_rate=lowercase_							, padding_value=lowercase_							, **lowercase_)

   _UpperCamelCase           =    feature_size
   _UpperCamelCase           =    sampling_rate
   _UpperCamelCase           =    padding_value
   _UpperCamelCase           =    hop_length
   _UpperCamelCase           =    win_length
   _UpperCamelCase           =    frame_signal_scale
   _UpperCamelCase           =    preemphasis_coeff
   _UpperCamelCase           =    mel_floor
   _UpperCamelCase           =    normalize_means
   _UpperCamelCase           =    normalize_vars
   _UpperCamelCase           =    win_function
   _UpperCamelCase           =    return_attention_mask

   _UpperCamelCase           =    win_length * sampling_rate // 1000
   _UpperCamelCase           =    hop_length * sampling_rate // 1000

   _UpperCamelCase           =    optimal_fft_length(self.sample_size)
   _UpperCamelCase           =    (self.n_fft // 2) + 1







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   np.array)						->	np.ndarray:


   """simple docstring"""







   if self.win_function == "hamming_window":
     _UpperCamelCase           =    window_function(window_length=self.sample_size							, name=self.win_function							, periodic=lowercase_)
   else:
     _UpperCamelCase           =    window_function(window_length=self.sample_size							, name=self.win_function)

   _UpperCamelCase           =    mel_filter_bank(
       num_frequency_bins=self.n_freqs							, num_mel_filters=self.feature_size							, min_frequency=0.0							, max_frequency=self.sampling_rate / 2.0							, sampling_rate=self.sampling_rate							, )

   _UpperCamelCase           =    spectrogram(
       one_waveform * self.frame_signal_scale							, window=lowercase_							, frame_length=self.sample_size							, hop_length=self.sample_stride							, fft_length=self.n_fft							, center=lowercase_							, preemphasis=self.preemphasis_coeff							, mel_filters=lowercase_							, mel_floor=self.mel_floor							, log_mel="log"							, )
   return msfc_features.T







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   int							, lowercase_				:   str							, lowercase_				:   Dict)						->	Any:


   """simple docstring"""







   if self.normalize_means:
     _UpperCamelCase           =    x[:input_length].mean(axis=0)
     _UpperCamelCase           =    np.subtract(lowercase_							, lowercase_)
   if self.normalize_vars:
     _UpperCamelCase           =    x[:input_length].std(axis=0)
     _UpperCamelCase           =    np.divide(lowercase_							, lowercase_)

   if input_length < x.shape[0]:
     _UpperCamelCase           =    padding_value

   # make sure array is in float32
   _UpperCamelCase           =    x.astype(np.floataa)

   return x







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   List[np.ndarray]							, lowercase_				:   Optional[np.ndarray] = None)						->	List[np.ndarray]:


   """simple docstring"""







   _UpperCamelCase           =    attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
   return [self._normalize_one(lowercase_							, lowercase_							, self.padding_value) for x, n in zip(lowercase_							, lowercase_)]







 def __call__(		self				:   Any							, lowercase_				:   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							, lowercase_				:   Union[bool, str, PaddingStrategy] = False							, lowercase_				:   Optional[int] = None							, lowercase_				:   bool = False							, lowercase_				:   Optional[int] = None							, lowercase_				:   Optional[bool] = None							, lowercase_				:   Optional[Union[str, TensorType]] = None							, lowercase_				:   Optional[int] = None							, **lowercase_				:   Union[str, Any]							, )						->	BatchFeature:


   """simple docstring"""







   if sampling_rate is not None:
     if sampling_rate != self.sampling_rate:
       raise ValueError(
           f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
           f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
           f' {self.sampling_rate} and not {sampling_rate}.')
   else:
     logger.warning(
         "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
         "Failing to do so can result in silent errors that might be hard to debug.")

   _UpperCamelCase           =    isinstance(lowercase_							, np.ndarray) and len(raw_speech.shape) > 1
   if is_batched_numpy and len(raw_speech.shape) > 2:
     raise ValueError(f'Only mono-channel audio is supported for input to {self}')
   _UpperCamelCase           =    is_batched_numpy or (
       isinstance(lowercase_							, (list, tuple)) and (isinstance(raw_speech[0]							, (np.ndarray, tuple, list)))
   )

   if is_batched:
     _UpperCamelCase           =    [np.asarray(lowercase_							, dtype=np.floataa) for speech in raw_speech]
   elif not is_batched and not isinstance(lowercase_							, np.ndarray):
     _UpperCamelCase           =    np.asarray(lowercase_							, dtype=np.floataa)
   elif isinstance(lowercase_							, np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
     _UpperCamelCase           =    raw_speech.astype(np.floataa)

   # always return batch
   if not is_batched:
     _UpperCamelCase           =    [raw_speech]

   # extract fbank features
   _UpperCamelCase           =    [self._extract_mfsc_features(lowercase_) for one_waveform in raw_speech]

   # convert into correct format for padding
   _UpperCamelCase           =    BatchFeature({"input_features": features})

   _UpperCamelCase           =    self.pad(
       lowercase_							, padding=lowercase_							, max_length=lowercase_							, truncation=lowercase_							, pad_to_multiple_of=lowercase_							, return_attention_mask=lowercase_							, **lowercase_							, )
   # make sure list is in array format
   _UpperCamelCase           =    padded_inputs.get("input_features")
   if isinstance(input_features[0]							, lowercase_):
     _UpperCamelCase           =    [np.asarray(lowercase_							, dtype=np.floataa) for feature in input_features]

   _UpperCamelCase           =    padded_inputs.get("attention_mask")
   if attention_mask is not None:
     _UpperCamelCase           =    [np.asarray(lowercase_							, dtype=np.intaa) for array in attention_mask]

   if self.normalize_means or self.normalize_vars:
     _UpperCamelCase           =    (
         np.array(lowercase_							, dtype=np.intaa)
         if self._get_padding_strategies(lowercase_							, max_length=lowercase_) is not PaddingStrategy.DO_NOT_PAD
         and padding
         else None
     )
     _UpperCamelCase           =    self.normalize(
         padded_inputs["input_features"]							, attention_mask=lowercase_)

   if return_tensors is not None:
     _UpperCamelCase           =    padded_inputs.convert_to_tensors(lowercase_)

   return padded_inputs





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''LongformerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''LongformerForMaskedLM''',
       '''LongformerForMultipleChoice''',
       '''LongformerForQuestionAnswering''',
       '''LongformerForSequenceClassification''',
       '''LongformerForTokenClassification''',
       '''LongformerModel''',
       '''LongformerPreTrainedModel''',
       '''LongformerSelfAttention''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFLongformerForMaskedLM''',
       '''TFLongformerForMultipleChoice''',
       '''TFLongformerForQuestionAnswering''',
       '''TFLongformerForSequenceClassification''',
       '''TFLongformerForTokenClassification''',
       '''TFLongformerModel''',
       '''TFLongformerPreTrainedModel''',
       '''TFLongformerSelfAttention''',
   ]


if TYPE_CHECKING:
   from .configuration_longformer import (
       LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       LongformerConfig,
       LongformerOnnxConfig,
   )
   from .tokenization_longformer import LongformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_longformer_fast import LongformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_longformer import (
          LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          LongformerForMaskedLM,
          LongformerForMultipleChoice,
          LongformerForQuestionAnswering,
          LongformerForSequenceClassification,
          LongformerForTokenClassification,
          LongformerModel,
          LongformerPreTrainedModel,
          LongformerSelfAttention,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_longformer import (
          TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLongformerForMaskedLM,
          TFLongformerForMultipleChoice,
          TFLongformerForQuestionAnswering,
          TFLongformerForSequenceClassification,
          TFLongformerForTokenClassification,
          TFLongformerModel,
          TFLongformerPreTrainedModel,
          TFLongformerSelfAttention,
      )

else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




